Search Results (377)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Lighting Systems (LSs) are a key component of modern cities. Across the years, thousands of articles have been published on this topic; nevertheless, a map of the state of the art of the extant literature is lacking. The present review reports on an analysis of the network of the co-occurrences of the authors’ keywords from 12,148 Scopus-indexed articles on LSs published between 1995 and 2024. This review addresses the following research questions: (RQ1) What are the major topics explored by scholars in connection with LSs within the 1995–2024 time frame? (RQ2) How do they group together? The investigation leveraged VOSviewer, an open-source software largely used for performing bibliometric analyses. The number of thematic clusters returned by VOSviewer was determined by the value of the minimum number of occurrences needed for the authors’ keywords to be admitted into the business analysis. If such a number is not properly chosen, the consequence is a set of clusters that do not represent meaningful patterns of the input dataset. In the present study, to overcome this issue, the threshold value balanced the score of four independent clustering validity indices against the authors’ judgment of a meaningful partition of the input dataset. In addition, our review delved into the impact that the use/non-use of a thesaurus of the authors’ keywords had on the number and composition of the thematic clusters returned by VOSviewer and, ultimately, on how this choice affected the correctness of the interpretation of the clusters. The study adhered to a well-known protocol, whose implementation is reported in detail. Thus, the workflow is transparent and replicable. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Using graphs as a data structure is a simple way to represent relationships between objects. Consequently, it has raised the need for algorithms to process, analyze, and extract meaningful information from graphs. Therefore, frequent subgraph mining (FSM) algorithms have been reported in the literature to discover interesting, unexpected, and useful patterns in graph databases. Frequent subgraph mining involves discovering subgraphs that appear no less than a user-specified threshold; this can be performed exactly or approximately. Although several algorithms for mining frequent approximate subgraphs exist, mining this type of subgraph in graph collections has scarcely been addressed. Thus, we propose AGCM-SLV, an algorithm for mining frequent approximate subgraphs within a graph collection that allows structural and label variations. Unlike other FSM approaches, our proposed algorithm tracks subgraph occurrences and their structural dissimilarities, allowing user-defined partial similarities between node and edge labels, and captures frequent approximate subgraphs (patterns) that would otherwise be overlooked. Experiments on real-world datasets demonstrate that our algorithm identifies more patterns than the most similar state-of-the-art algorithm with a shorter runtime. We also present experiments in which we add white noise to the graph collection at different levels, revealing that over 99% of the patterns extracted without noise are preserved under noisy conditions, making the proposed algorithm noise-tolerant. Full article

The documentation of cultural heritage, particularly built heritage, represents a critical component in ensuring its preservation, sustainable management, and effective transmission to future generations. As the field increasingly undergoes a digital transformation, there is a growing need for structured, standardised approaches that can guide professionals and stakeholders through the complexities of documentation practices. Despite the availability of numerous standards and charters, a clear synthesis of consolidated methodologies and recent technological shifts remains limited. This study addresses this gap by conducting a Systematic Literature Review (SLR) to assess current documentation practices. The research is part of a larger initiative funded by the FSE REACT-EU programme under the Italian PON Ricerca e Innovazione 2014–2020, specifically aiming to support public and private stakeholders in developing practical documentation strategies. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, over 266 publications were analysed to reconstruct the state of the art. The findings confirm widely adopted practices among research groups while also highlighting emerging trends driven by technological advancements in geomatics. These insights will contribute to the formulation of practical guidelines to support operators in the field and reinforce the integration of innovative tools in Cultural Heritage documentation workflows. Full article

Proton-conducting ceramics have gained significant attention in various applications. Yttrium-doped barium cerium zirconate (BaCe_xZr_1−x−yY_yO_3–δ) is the state-of-the-art proton-conducting electrolyte but poses a major challenge because of its high sintering temperature. Sintering aids have been found to substantially reduce the sintering temperature of BaCe_xZr_1−x−yY_yO_3–δ. This work evaluates, for the first time, the impact of NiO and ZnO addition in three different loadings (1, 3, 5 mol%), via wet mechanical mixing, on the sintering and electrical properties of a low cerium-containing composition, BaCe_0.2Zr_0.7Y_0.1O_3–δ (BCZY). The sintering temperature remarkably dropped from 1600 °C (for pure BCZY) to 1350 °C (for NiOBCZY and ZnOBCZY) while achieving > 95% densification. In general, ZnO gave higher densification than NiO, the highest being 99% for 5 mol% ZnOBCZY. Dilatometric studies revealed that ZnOBCZY attained complete shrinkage at temperatures lower than NiOBCZY. Up to 650 °C, ZnO showed higher conductivity compared to NiO for the same loading, mostly due to a higher extent of Zn incorporation inside the BCZY lattice as seen from the BCZY peak shift to a lower Bragg’s angle in X-ray diffractograms, and the bigger grain sizes of ZnO samples compared to NiO captured in scanning electron microscopy. At any temperature, the variation in conductivity as a function of sintering aid concentration followed the orders 1 mol% > 3 mol% > 5 mol% (for ZnO) and 1 mol% < 3 mol%~5 mol% (for NiO). This difference in conductivity trends has been attributed to the fact that Zn fully dissolves into the BCZY matrix, unlike NiO which mostly accumulates at the grain boundaries. At 600 °C, 1 mol% ZnOBCZY showed the highest conductivity of 5.02 mS/cm, which is, by far, higher than what has been reported in the literature for a Ce/Zr molar ratio <1. This makes ZnO a better sintering aid than NiO (in the range of 1 to 5 mol% addition) in terms of higher densification at a sintering temperature as low as 1350 °C, and higher conductivity. Full article

The rapid proliferation of Internet of Things (IoT) devices presents significant security challenges due to inherent vulnerabilities and increasing cyberattacks. Effective intrusion detection systems (IDSs) are crucial for securing IoT environments, requiring high detection accuracy while efficiently handling large data volumes and critically minimizing both false negatives (FNR), which miss real attacks, and false positives (FPR), which cause unnecessary alarms. Traditional methods and standalone deep learning models often struggle to achieve an optimal balance between these requirements. This paper proposes a novel hybrid IDS framework designed to enhance IoT network security by integrating time series analysis with deep learning. Specifically, we leverage seasonal-trend decomposition using Loess (STL) as an intelligent pre-filtering mechanism to isolate potentially anomalous traffic, which is then classified using sophisticated deep learning models, with a particular focus on long short-term memory (LSTM) networks compared against recurrent neural networks (RNN) and dense neural networks (DNN). The proposed framework performance was rigorously evaluated using the comprehensive and realistic BoT-IoT dataset. The methodology involved principal component analysis (PCA) for dimensionality reduction and careful hyperparameter tuning. The experimental results demonstrate the effectiveness of the hybrid approach, with the STL-LSTM variant achieving superior performance. It attained an overall accuracy of 98.5% and a macro F1-score of 98.49%, while significantly reducing the overall false negative rate to 5.2% (FNR ≈ 0.24) and the overall false positive rate to 0.276% (FPR ≈ 0.11) across five Attack/Normal classes. These results represent a substantial improvement over standalone deep learning models (standalone LSTM FNR = 0.302, FPR = 0.185) and compare favorably to state-of-the-art benchmarks reported in the literature, particularly in minimizing critical detection errors. The findings indicate that the proposed hybrid STL-LSTM framework presents a robust and viable solution for high-stakes IoT network security, effectively balancing high detection accuracy with exceptionally low error rates, making it well-suited for real-time deployment in protecting critical IoT infrastructure. Full article

Solar photovoltaic (PV) technology has emerged as the most preferred source of clean energy generation and has been deployed at a large scale. However, end-of-life management of the PV modules is a critical issue that has garnered the recent attention of lawmakers and researchers alike. Consequently, several researchers are actively developing technology to recycle the end-of-life PV modules. Since silicon PV modules account for more than 90% of the modules deployed globally, most of these efforts are focused on recycling crystalline silicon PV modules. Researchers have primarily focused on recovering pure silver from the contacts and pure Si from the solar cells. However, to ensure complete recyclability of such panels, the different polymers used in these modules must also be recycled. This review addresses the issue of recycling the polymers from end-of-life c-Si modules. Scopus and Google Scholar were used to search for the relevant literature. This review presents the current state-of-the-art technology related to polymer recycling found in the PV modules, the challenges encountered in their recycling, and the outlook. While research on the recycling of polymers has progressed in the last few decades, the instances of their applications in the recycling of polymers from PV panels are rarely reported in the literature. In this work, certain technical pathways, which can be employed to recycled polymers obtained from end-of-life PV panels, are presented. Recycling the polymers from the end-of-life silicon PV modules is crucial for improving the sustainability of solar PV technology. Full article

The agricultural sector is undergoing a profound transformation driven by the integration of innovative technologies and practices, but the adoption of these technologies remains uneven. Holistic approaches to the diffusion of innovative technologies in agriculture are seen as crucial for effective adoption and sustainable development. In this context, the systemic dimension of technology adoption is characterized by the interactions between actors that create knowledge and promote the process of technology adoption. Therefore, the overall objective of this study is to provide a comprehensive analysis of the current state of the art in relation to the systemic dimension of the process of technology adoption in developed countries. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension protocol for scoping reviews, we examined the literature to capture the role of the systems dimension in the process of technology adoption. We conducted a two-analysis, bibliometric and content network analysis to identify the concepts and thematic clusters that define the systemic dimension and represent the main drivers of technology adoption for sustainable development in agriculture. The results show that the factors influencing the adoption of agricultural technologies are treated inconsistently in the literature, with a focus on technological and economic aspects rather than systemic elements such as governance and stakeholder interactions. Full article

Identifying blueberry characteristics such as the wax bloom is an important task that not only helps in phenotyping (for novel variety development) but also in classifying berries better suited for commercialization. Deep learning techniques for image analysis have long demonstrated their capability for solving image classification problems. However, they usually rely on large architectures that could be difficult to implement in the field due to high computational needs. This paper presents a small (only 1502 parameters) Bayesian–CNN ensemble architecture that can be implemented in any small electronic device and is able to classify wax bloom content in images. The Bayesian model was implemented using Keras image libraries and consists of only two convolutional layers (eight and four filters, respectively) and a dense layer. It includes a statistical module with two metrics that combines the results of the Bayesian ensemble to detect potential misclassifications. The first metric is based on the Euclidean distance (${\mathcal{L}}_2$) between Gaussian mixture models while the second metric is based on a quantile analysis of the binary class predictions. Both metrics attempt to establish whether the model was able to find a good prediction or not. Three experiments were performed: first, the Bayesian–CNN ensemble model was compared with state-of-the-art small architectures. In experiment 2, the metrics for detecting potential misclassifications were evaluated and compared with similar techniques derived from the literature. Experiment 3 reports results while using cross validation and compares performance considering the trade-off between accuracy and the number of samples considered as potentially misclassified (not classified). Both metrics show a competitive performance compared to the state of the art and are able to improve the accuracy of a Bayesian–CNN ensemble model from $96.98\%$ to $98.72\pm 0.54\%$ and $98.38\pm 0.34\%$ for the ${\mathcal{L}}_2$ and $r^2$ metrics, respectively. Full article

Background: Interdialytic weight gain (IDWG), defined as the accumulation of salt and water intake between dialysis sessions, is a critical parameter of fluid management and a marker of adherence to dietary and fluid restrictions in hemodialysis patients. Excessive IDWG has been strongly associated with increased cardiovascular risk, including left ventricular hypertrophy, cardiac dysfunction, and cerebrovascular complications. Additionally, it necessitates more aggressive ultrafiltration, potentially compromising hemodynamic stability, impairing quality of life, and escalating healthcare costs. Despite international guidelines recommending an IDWG target of <4–4.5% of body weight, many patients struggle to achieve this due to barriers in adhering to dietary and fluid restrictions. This review explores the current state-of-the-art strategies to mitigate IDWG and evaluates emerging diagnostic and therapeutic perspectives to improve fluid management in dialysis patients. Methods: A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science to identify studies on IDWG in hemodialysis. Keywords and MeSH terms were used to retrieve peer-reviewed articles, observational studies, RCTs, meta-analyses, and systematic reviews. Non-English articles, case reports, and conference abstracts were excluded. Study selection followed PRISMA guidelines, with independent screening of titles, abstracts, and full texts. Data extraction focused on IDWG definitions, risk factors, clinical outcomes, and management strategies. Due to study heterogeneity, a narrative synthesis was performed. Relevant data were synthesized thematically to evaluate both established strategies and emerging perspectives. Results: The current literature identifies three principal strategies for IDWG control: cognitive–behavioral interventions, dietary sodium restriction, and dialysis prescription adjustments. While educational programs and behavioral counseling improve adherence, their long-term effectiveness remains constrained by patient compliance and logistical challenges. Similarly, low-sodium diets, despite reducing thirst, face barriers to adherence and potential nutritional concerns. Adjustments in dialysate sodium concentration have yielded conflicting results, with concerns regarding hemodynamic instability and intradialytic hypotension. Given these limitations, alternative approaches are emerging. Thirst modulation strategies, including chewing gum to stimulate salivation and acupuncture for autonomic regulation, offer potential benefits in reducing excessive fluid intake. Additionally, technological innovations, such as mobile applications and telemonitoring, enhance self-management by providing real-time feedback on fluid intake. Biofeedback-driven dialysis systems enable dynamic ultrafiltration adjustments, improving fluid removal efficiency while minimizing hemodynamic instability. Artificial intelligence (AI) is advancing predictive analytics by integrating wearable bioimpedance sensors and dialysis data to anticipate fluid overload and refine individualized dialysis prescriptions, driving precision-based volume management. Finally, optimizing dialysis frequency and duration has shown promise in achieving better fluid balance and cardiovascular stability, suggesting that a personalized, multimodal approach is essential for effective IDWG management. Conclusions: Despite decades of research, IDWG remains a persistent challenge in hemodialysis, requiring a multifaceted, patient-centered approach. While traditional interventions provide partial solutions, integrating thirst modulation strategies, real-time monitoring, biofeedback dialysis adjustments, and AI-driven predictive tools represent the next frontier in fluid management. Future research should focus on long-term feasibility, patient adherence, and clinical efficacy, ensuring these innovations translate into tangible improvements in quality of life and cardiovascular health for dialysis patients. Full article

Background/Objectives: Modern breast reconstruction has undergone substantial evolution, with implant-based, pedicled autologous, and free autologous techniques. The purpose of this study is to review the current state of the art in free autologous breast reconstruction, highlighting advancements in the types of flaps, donor site selection, techniques, and functional restoration. Methods: A literature review was conducted using PubMed to capture studies related to well-known free flaps that are used for breast reconstruction. Studies for each flap type were reviewed and sorted for inclusion into one of six categories: (1) clinical outcomes, (2) comparison studies of alternative flaps, (3) preoperative planning, (4) flap classifications and perfusion zones, (5) technique descriptions, and (6) time and cost analyses. Results: The majority (77%) of articles included were written on various types of abdominally based free flaps, including TRAM, DIEP, and SIEA flaps. These studies indicated an evolution in technique over time to minimize donor site morbidity, improve patient-reported and functional outcomes, improve efficiency, and expand clinical indications. The remaining 23% of articles discussed alternative flap choices, including PAP, TUG, S/IGAP, and LAP flaps. Studies highlighted technical challenges and the evolution of techniques to make these flaps more accessible, as well as how to combine flaps to expand clinical indications. Conclusions: Autologous breast reconstruction has evolved significantly, with advancements in techniques such as robotic-assisted surgery, multi-flap reconstruction, bipedicled flaps, and neurotization. This review highlights the current best practices while acknowledging ongoing challenges and the potential for future innovations in microsurgery, nerve regeneration, and personalized medicine, which hold promise for further refining outcomes. Full article

Old Cairo, also known as Islamic Cairo, is a UNESCO World Heritage Site representing a rich tapestry of history and culture. Today, among various significant aspects, its cultural heritage necessitates the elaboration of a proactive conservation strategy, which should take advantage of the intrinsic support provided by the efforts documented in the literature that have been made in several scientific fields, disciplines, and directions over the years. Most historic religious monumental buildings in Old Cairo, in particular, not only face the effects of local seismic hazards, which are emphasized by damage by past earthquakes, but also suffer the consequences of several influencing parameters that are unique to the Cairo city context. In this sense, it is known that the structural retrofitting of these monumental buildings requires sound knowledge of technical details and criticalities, based on inspections, numerical simulations, the in-field integration of technologies, and laboratory tests. Many other gaps should also be addressed, and a sound conservation strategy should be elaborated on the basis of a multi-target approach, which could account for the structural engineering perspective but also contextualize the retrofit within the state of the art and the evolution of past events. This is the target of the contemporary “Particular Relevance” bilateral Italy–Egypt “CoReng” project, seeking to define a multidisciplinary strategy for conserving Old Cairo’s cultural heritage and focusing primarily on the case study of the Religions Complex. To this end, a review analysis of major oversights and challenges relating to historic monuments in Old Cairo is presented in this paper. Learning from past accidents and experiences is, in fact, the primary supporting basis for elaborating new operational steps and efficient approaches to mitigating challenges and minimizing the consequences of emergency events. As such, this review contribution specifically focuses on the structural vulnerability of historic monumental buildings in Old Cairo, reporting on past efforts, past strategy proposals, research experiences, and trends. Full article

A serious game combines concepts, principles, and methods of game design with information and communication technologies for the achievement of a given goal beyond entertainment. Serious game studies have been reported under a brain–computer interface (BCI) approach, with the specific use of electroencephalographic (EEG) signals. This study presents a review of the technological solutions from existing works related to serious games and EEG signals. A taxonomy is proposed for the classification of the research literature in three different categories according to the experimental strategy for the integration of the game and EEG: (1) evoked signals, (2) spontaneous signals, and (3) hybrid signals. Some details and additional aspects of the studies are also reviewed. The analysis involves factors such as platforms and development languages (serious game), software tools (integration between serious game and EEG signals), and the number of test subjects. The findings indicate that 50% of the identified studies use spontaneous signals as the experimental strategy. Based on the definition, categorization, and state of the art, the main research challenges and future directions for this class of technological solutions are discussed. Full article

Pediatric pneumonia remains a significant global health issue, particularly in low- and middle-income countries, where it contributes substantially to mortality in children under five. This study introduces a deep learning model for pediatric pneumonia diagnosis from chest X-rays that surpasses the performance of state-of-the-art methods reported in the recent literature. Using a DenseNet201 architecture with a Mish activation function and multi-scale convolutions, the model was trained on a dataset of 5856 chest X-ray images, achieving high performance: 0.9642 accuracy, 0.9580 precision, 0.9506 sensitivity, 0.9542 F1 score, and 0.9507 specificity. These results demonstrate a significant advancement in diagnostic precision and efficiency within this domain. By achieving the highest accuracy and F1 score compared to other recent work using the same dataset, our approach offers a tangible improvement for resource-constrained environments where access to specialists and sophisticated equipment is limited. While the need for high-quality datasets and adequate computational resources remains a general consideration for deep learning applications, our model’s demonstrably superior performance establishes a new benchmark and offers the delivery of more timely and precise diagnoses, with the potential to significantly enhance patient outcomes. Full article